NIfTI Format Research Articles

A 3D U-NET BASED ON EARLY FUSION MODEL: IMPROVEMENT, COMPARATIVE ANALYSIS WITH STATE-OF-THE-ART MODELS AND FINE-TUNING

Multi-organ segmentation is the process of identifying and separating multiple organs in medical images. This segmentation allows for the detection of structural abnormalities by examining the morphological structure of organs. Carrying out the process quickly and precisely has become an important issue in today's conditions. In recent years, researchers have used various technologies for the automatic segmentation of multiple organs. In this study, improvements were made to increase the multi-organ segmentation performance of the 3D U-Net based fusion model combining HSV and grayscale color spaces and compared with state-of-the-art models. Training and testing were performed on the MICCAI 2015 dataset published at Vanderbilt University, which contains 3D abdominal CT images in NIfTI format. The model's performance was evaluated using the Dice similarity coefficient. In the tests, the liver organ showed the highest Dice score. Considering the average Dice score of all organs, and comparing it with other models, it has been observed that the fusion approach model yields promising results.

Open access segmentations of intraoperative brain tumor ultrasound images.

Registration and segmentation of magnetic resonance (MR) and ultrasound (US) images could play an essential role in surgical planning and resectioning brain tumors. However, validating these techniques is challenging due to the scarcity of publicly accessible sources with high-quality ground truth information. To this end, we propose a unique set of segmentations (RESECT-SEG) of cerebral structures from the previously published RESECT dataset to encourage a more rigorous development and assessment of image-processing techniques forneurosurgery. The RESECT database consists of MR and intraoperative US (iUS) images of 23 patients who underwent brain tumor resection surgeries. The proposed RESECT-SEG dataset contains segmentations of tumor tissues, sulci, falx cerebri, and resection cavity of the RESECT iUS images. Two highly experienced neurosurgeons validated the quality of the segmentations. Segmentations are provided in 3D NIFTI format in the OSF open-science platform: https://osf.io/jv8bk. The proposed RESECT-SEG dataset includes segmentations of real-world clinical US brain images that could be used to develop and evaluate segmentation and registration methods. Eventually, this dataset could further improve the quality of image guidance inneurosurgery.

Development and Implementation of an Innovative Framework for Automated Radiomics Analysis in Neuroimaging.

Radiomics represents an innovative approach to medical image analysis, enabling comprehensive quantitative evaluation of radiological images through advanced image processing and Machine or Deep Learning algorithms. This technique uncovers intricate data patterns beyond human visual detection. Traditionally, executing a radiomic pipeline involves multiple standardized phases across several software platforms. This could represent a limit that was overcome thanks to the development of the matRadiomics application. MatRadiomics, a freely available, IBSI-compliant tool, features its intuitive Graphical User Interface (GUI), facilitating the entire radiomics workflow from DICOM image importation to segmentation, feature selection and extraction, and Machine Learning model construction. In this project, an extension of matRadiomics was developed to support the importation of brain MRI images and segmentations in NIfTI format, thus extending its applicability to neuroimaging. This enhancement allows for the seamless execution of radiomic pipelines within matRadiomics, offering substantial advantages to the realm of neuroimaging.

Generating Synthetic Radiological Images with PySynthMRI: An Open-Source Cross-Platform Tool.

Synthetic MR Imaging allows for the reconstruction of different image contrasts from a single acquisition, reducing scan times. Commercial products that implement synthetic MRI are used in research. They rely on vendor-specific acquisitions and do not include the possibility of using custom multiparametric imaging techniques. We introduce PySynthMRI, an open-source tool with a user-friendly interface that uses a set of input images to generate synthetic images with diverse radiological contrasts by varying representative parameters of the desired target sequence, including the echo time, repetition time and inversion time(s). PySynthMRI is written in Python 3.6, and it can be executed under Linux, Windows, or MacOS as a python script or an executable. The tool is free and open source and is developed while taking into consideration the possibility of software customization by the end user. PySynthMRI generates synthetic images by calculating the pixelwise signal intensity as a function of a set of input images (e.g., T1 and T2 maps) and simulated scanner parameters chosen by the user via a graphical interface. The distribution provides a set of default synthetic contrasts, including T1w gradient echo, T2w spin echo, FLAIR and Double Inversion Recovery. The synthetic images can be exported in DICOM or NiFTI format. PySynthMRI allows for the fast synthetization of differently weighted MR images based on quantitative maps. Specialists can use the provided signal models to retrospectively generate contrasts and add custom ones. The modular architecture of the tool can be exploited to add new features without impacting the codebase.

Comparative Analysis of Image Processing Techniques for Enhanced MRI Image Quality: 3D Reconstruction and Segmentation Using 3D U-Net Architecture.

Osteosarcoma is a common type of bone tumor, particularly prevalent in children and adolescents between the ages of 5 and 25 who are experiencing growth spurts during puberty. Manual delineation of tumor regions in MRI images can be laborious and time-consuming, and results may be subjective and difficult to replicate. Therefore, a convolutional neural network (CNN) was developed to automatically segment osteosarcoma cancerous cells in three types of MRI images. The study consisted of five main stages. First, 3692 DICOM format MRI images were acquired from 46 patients, including T1-weighted, T2-weighted, and T1-weighted with injection of Gadolinium (T1W + Gd) images. Contrast stretching and median filter were applied to enhance image intensity and remove noise, and the pre-processed images were reconstructed into NIfTI format files for deep learning. The MRI images were then transformed to fit the CNN's requirements. A 3D U-Net architecture was proposed with optimized parameters to build an automatic segmentation model capable of segmenting osteosarcoma from the MRI images. The 3D U-Net segmentation model achieved excellent results, with mean dice similarity coefficients (DSC) of 83.75%, 85.45%, and 87.62% for T1W, T2W, and T1W + Gd images, respectively. However, the study found that the proposed method had some limitations, including poorly defined borders, missing lesion portions, and other confounding factors. In summary, an automatic segmentation method based on a CNN has been developed to address the challenge of manually segmenting osteosarcoma cancerous cells in MRI images. While the proposed method showed promise, the study revealed limitations that need to be addressed to improve its efficacy.

SynthRAD2023 Grand Challenge dataset: Generating synthetic CT for radiotherapy.

Medical imaging has become increasingly important in diagnosing and treating oncological patients, particularly in radiotherapy. Recent advances in synthetic computed tomography (sCT) generation have increased interest in public challenges to provide data and evaluation metrics for comparing different approaches openly. This paper describes a dataset of brain and pelvis computed tomography (CT) images with rigidly registered cone-beam CT(CBCT) and magnetic resonance imaging (MRI) images to facilitate the development and evaluation of sCT generation for radiotherapy planning. The dataset consists of CT, CBCT, and MRI of 540 brains and 540 pelvic radiotherapy patients from three Dutch university medical centers. Subjects' ages ranged from 3 to 93 years, with a mean age of 60. Various scanner models and acquisition settings were used across patients from the three data-providing centers. Details are available in a comma separated value files provided with the datasets. The data is available on Zenodo (https://doi.org/10.5281/zenodo.7260704, https://doi.org/10.5281/zenodo.7868168) under the SynthRAD2023 collection. The images for each subject are available in nifti format. This dataset will enable the evaluation and development of image synthesis algorithms for radiotherapy purposes on a realistic multi-center dataset with varying acquisition protocols. Synthetic CT generation has numerous applications in radiation therapy, including diagnosis, treatment planning, treatment monitoring, and surgical planning.

ORYX‐MRSI: A fully‐automated open‐source software for proton magnetic resonance spectroscopic imaging data analysis

AbstractProton magnetic resonance spectroscopic imaging (1H‐MRSI) provides a noninvasive, spatially resolved evaluation of brain metabolism. However, there are some limitations of 1H‐MRSI preventing its wider use in the clinics, including the spectral quality issues, partial volume effect and chemical shift artifact. Additionally, it is necessary to create metabolite maps for analyzing spectral data along with other MRI modalities. In this study, a MATLAB‐based open‐source data analysis software for three‐dimensional 1H‐MRSI, called Oryx‐MRSI, which includes modules for visualization of raw 1H‐MRSI data and LCModel outputs, chemical shift correction, tissue fraction calculation, metabolite map production, and registration onto standard MNI152 brain atlas while providing automatic spectral quality control, is presented. Oryx‐MRSI implements region of interest analysis at brain parcellations defined on MNI152 brain atlas. All generated metabolite maps are stored in NIfTI format. Oryx‐MRSI is publicly available at https://github.com/Computational-Imaging-LAB/Oryx-MRSI along with six example datasets.

Multi-modal magnetic resonance imaging in a mouse model of concussion

This data collection contains Magnetic Resonance Imaging (MRI) data, including structural, diffusion, stimulus-evoked, and resting-state functional MRI and behavioural assessment results, including acute post-impact Loss-of-Righting Reflex time and acute, subacute, and longer-term Neural Severity Score, and Open Field Behaviour obtained from a mouse model of concussion. Four cohorts with 43 3–4 months old male mice in total were used: Sham (n = 14, n = 6 day 2, n = 3 day 7, n = 5 day 14), concussion day 2 (CON 2; n = 9), concussion day 7 (CON 7; n = 10), concussion day 14 (CON 14; n = 10). The data collection contains the aforementioned MRI data in compressed NIFTI format, data sheets on animal’s backgrounds and behavioural outcomes and is made publicly available from a data repository. The available data are intended to facility cross-study comparisons, meta-analysis, and science reproducibility.

Machine learning-based texture analysis for differentiation of radiologically indeterminate small adrenal tumors on adrenal protocol CT scans.

To evaluate the ability of radiomic feature extraction and a machine learning algorithm to differentiate between benign and malignant indeterminate adrenal lesions on contrast-enhanced computed tomography (CT) studies. Adrenal "incidentalomas" are adrenal lesions that are accidentally discovered during workup not related to the adrenal glands; they have an incidence as high as 5%. Small adrenal incidentalomas (< 4cm) with high attenuation values on pre-contrast CT(> 10 HU) need further evaluation to calculate the absolute percentage of washout (APW). If the APW is < 60%, these lesions are considered non-adenomas and commonly classified as indeterminate adrenal lesions. Further workup for indeterminate lesions includes more complicated and expensive radiological studies or invasive procedures like biopsy or surgical resection. We searched our institutional database for indeterminate adrenal lesions with the following characteristics: < 4cm, pre-attenuation value > 10 HU, and APW < 60%. Exclusion criteria included pheochromocytoma and no histopathological examination. CT images were converted to Nifti format, and adrenal tumors were segmented using Amira software. Radiomic features from the adrenal mask were extracted using PyRadiomics software after removing redundant features (highly pairwise correlated features and low-variance features) using recursive feature extraction to select the final discriminative set of features. Lastly, the final features were used to build a binary classification model using a random forest algorithm, which was validated and tested using leave-one-out cross-validation, confusion matrix, and receiver operating characteristic curve. We found 40 indeterminate adrenal lesions (21 benign and 19 malignant). Feature extraction resulted in 3947 features, which reduced down to 62 features after removing redundancies. Recursive feature elimination resulted in the following top 4 discriminative features: gray-level size zone matrix-derived size zone non-uniformity from pre-contrast and delayed phases, gray-level dependency matrix-derived large dependence high gray-level emphasis from venous-phase, and gray-level co-occurrence matrix-derived cluster shade from delayed-phase. A binary classification model with leave-one-out cross-validation showed AUC = 0.85, sensitivity = 84.2%, and specificity = 71.4%. Machine learning and radiomic features extraction can differentiate between benign and malignant indeterminate adrenal tumors and can be used to direct further workup with high sensitivity and specificity.

PleThora: Pleural effusion and thoracic cavity segmentations in diseased lungs for benchmarking chest CT processing pipelines.

This manuscript describes a dataset of thoracic cavity segmentations and discrete pleural effusion segmentations we have annotated on 402 computed tomography (CT) scans acquired from patients with non‐small cell lung cancer. The segmentation of these anatomic regions precedes fundamental tasks in image analysis pipelines such as lung structure segmentation, lesion detection, and radiomics feature extraction. Bilateral thoracic cavity volumes and pleural effusion volumes were manually segmented on CT scans acquired from The Cancer Imaging Archive “NSCLC Radiomics” data collection. Four hundred and two thoracic segmentations were first generated automatically by a U‐Net based algorithm trained on chest CTs without cancer, manually corrected by a medical student to include the complete thoracic cavity (normal, pathologic, and atelectatic lung parenchyma, lung hilum, pleural effusion, fibrosis, nodules, tumor, and other anatomic anomalies), and revised by a radiation oncologist or a radiologist. Seventy‐eight pleural effusions were manually segmented by a medical student and revised by a radiologist or radiation oncologist. Interobserver agreement between the radiation oncologist and radiologist corrections was acceptable. All expert‐vetted segmentations are publicly available in NIfTI format through The Cancer Imaging Archive at https://doi.org/10.7937/tcia.2020.6c7y‐gq39. Tabular data detailing clinical and technical metadata linked to segmentation cases are also available. Thoracic cavity segmentations will be valuable for developing image analysis pipelines on pathologic lungs — where current automated algorithms struggle most. In conjunction with gross tumor volume segmentations already available from “NSCLC Radiomics,” pleural effusion segmentations may be valuable for investigating radiomics profile differences between effusion and primary tumor or training algorithms to discriminate between them.

Towards Optimising MRI Characterisation of Tissue (TOMCAT) Dataset including all Longitudinal Automatic Segmentation of Hippocampal Subfields (LASHiS) data.

Seven healthy participants were scanned using a Siemens Magnetom 7 Tesla (T) whole-body research MRI scanner (Siemens Healthcare, Erlangen, Germany). The first scan session was acquired in 2016 (time point one), the second and third session in 2019 (time point two and three, respectively) with the third session acquired 45 min following the second as a scan-rescan condition. The following scans were acquired for all time points: structural T1 weighted (T1w) MP2RAGE, high in-plane resolution Turbo-Spin Echo (TSE) dedicated for hippocampus subfield segmentation. The data were used in three projects to date, for more insight see: 1) Non-linear realignment for Turbo-Spin Echo retrospective motion correction and hippocampus segmentation improvement [1] 2) Longitudinal Automatic Segmentation of Hippocampal Subfields (LASHiS) using multi-contrast MRI [2]. 3) The challenge of bias-free coil combination for quantitative susceptibility mapping at ultra-high field [3]. Data were converted from DICOM to nifti format following the Brain Imaging Data Structure (BIDS) [4]. Data were analysed for the accompanying manuscript “Longitudinal Automatic Segmentation of Hippocampal Subfields (LASHiS) using multi-contrast MRI” including test-retest reliability and longitudinal Bayesian Linear Mixed Effects (LME) modelling.

The Brain/MINDS 3D digital marmoset brain atlas

We present a new 3D digital brain atlas of the non-human primate, common marmoset monkey (Callithrix jacchus), with MRI and coregistered Nissl histology data. To the best of our knowledge this is the first comprehensive digital 3D brain atlas of the common marmoset having normalized multi-modal data, cortical and sub-cortical segmentation, and in a common file format (NIfTI). The atlas can be registered to new data, is useful for connectomics, functional studies, simulation and as a reference. The atlas was based on previously published work but we provide several critical improvements to make this release valuable for researchers. Nissl histology images were processed to remove illumination and shape artifacts and then normalized to the MRI data. Brain region segmentation is provided for both hemispheres. The data is in the NIfTI format making it easy to integrate into neuroscience pipelines, whereas the previous atlas was in an inaccessible file format. We also provide cortical, mid-cortical and white matter boundary segmentations useful for visualization and analysis.

Neuroanatomic Volumes as Potential Predictors of Cognitive Outcomes in Pediatric Medulloblastoma Survivors: A Quantitative MRI Study

Proton RT helps to mitigate the adverse neurocognitive effects of radiotherapy in pediatric medulloblastoma patients. Here, we investigate the relationship of neuroanatomical volumes globally with cognitive late effects, using FSIQ and its four component scores. We hypothesize that sub-volumes of the left temporal lobe will be particularly implicated in cognitive outcomes. Our cohort includes 59 prospectively-followed pediatric medulloblastoma patients (median age at proton RT = 6.8, range 3.5-22.1) from a recently published Phase II clinical trial (Yock et al., Lancet Oncol2016). All available high-resolution Axial T1 and MPRAGE MRI series images were converted to NIfTI format and processed by 1x1x1 voxel resampling, field-of-view normalization, multi-atlas skull stripping, and autosegmentation into 83 independent regions. Univariate linear regression was performed to investigate the relationship between regional volumes and IQ scores at similar time-points, and Pearson correlation coefficients were calculated. Multivariate linear regression results will be presented. 306 brain MRI images and 152 neurocognitive evaluations were available for analysis. Of these, 115 pairs of T1/MPRAGE images and neurocognitive scores were identified, with a restriction of suitable pairs such that an imaging study was acquired ±1 year from the cognitive evaluation date. All MRI-IQ pairs were considered independently from one another, even for multiple measures of the same patient. A smaller subset of images was processed at the time of submission, while the full dataset will be presented. On univariate analysis, the following regional volumes were most correlated with corresponding cognitive measures: FSIQ and the L caudate nucleus (r = -0.56), verbal comprehension index and the R fusiform gyrus (r = +0.58), perceptual reasoning index and the L caudate nucleus (r = -0.72), working memory and the R inferolateral parietal lobe (r = -0.72), and processing speed and the R inferior/middle temporal gyrus (r = +0.65). In this preliminary analysis, we have applied rigorous computational techniques to the quantitative analysis of clinical imaging studies for medulloblastoma patients. The subset of cases with completed analysis are not conclusive and preliminary results appear to not support our hypothesis. We expect the analysis of the full dataset to provide more insight. We further anticipate that multivariate analysis will help us better explore interaction and connectivity among various brain regions, as well as identify a subset of regions that—when considered together—best predict FSIQ and its components. Future work will involve inclusion of age- and gender-matched normal controls to determine if these results extend beyond patients treated with craniospinal irradiation for medulloblastoma.

Bruker2nifti: Magnetic Resonance Images converter from Bruker ParaVision to Nifti format

In clinical and pre-clinical research involving medical images, the first step following a Magnetic Resonance Imaging dataset acquisition, usually entails the conversion of image data from the native scanner format to a format suitable for the intended analysis. The proprietary [Bruker ParaVision](https://www.bruker.com/products/mr/preclinical-mri/software/service-support.html) software currently does not provide the tools for conversion of the data to suitable and open formats for research, such as nifti [@cox2004sort], for which most of the available tools for medical image analysis are implemented. For this purpose we have designed and developed [bruker2nifti](https://github.com/SebastianoF/bruker2nifti), a pip-installable Python tool provided with a Graphical User Interface to convert from the native MRI Bruker format to the nifti format, without any intermediate step through the DICOM standard formats [@Mildenberger2002]. Bruker2nifti is intended to be a tool to access the data structure and to parse all parameter files of the Bruker ParaVision format into python dictionaries, to select the relevant information to fill the Nifti header and data volume. Lastly it is meant to be a starting point where to integrate possible future variations in Bruker hardware and ParaVision software future releases.

The first step for neuroimaging data analysis: DICOM to NIfTI conversion

BackgroundClinical imaging data are typically stored and transferred in the DICOM format, whereas the NIfTI format has been widely adopted by scientists in the neuroimaging community. Therefore, a vital initial step in processing the data is to convert images from the complicated DICOM format to the much simpler NIfTI format. While there are a number of tools that usually handle DICOM to NIfTI conversion seamlessly, some variations can disrupt this process. New methodWe provide some insight into the challenges faced with image conversion. First, different manufacturers implement the DICOM format differently which complicates the conversion. Second, different modalities and sub-modalities may need special treatment during conversion. Lastly, the image transferring and archiving can also impact the DICOM conversion. ResultsWe present results in several error-prone domains, including the slice order for functional imaging, phase encoding direction for distortion correction, effect of diffusion gradient direction, and effect of gantry correction for some imaging modality. Comparison with existing methodsConversion tools are often designed for a specific manufacturer or modality. The tools and insight we present here are aimed at different manufacturers or modalities. ConclusionsThe imaging conversion is complicated by the variation of images. An understanding of the conversion basics can be helpful for identifying the source of the error. Here we provide users with simple methods for detecting and correcting problems. This also serves as an overview for developers who wish to either develop their own tools or adapt the open source tools created by the authors.

SU-E-I-69: A Cloud Based Application for MRI Brain Image Processing

Purpose: In this study we present a cloud based application dedicated to MRI brain image processing, providing the flexibility to end users for managing and processing medical image data, dynamically allocating computational resources. Methods: The proposed application is based on the Software as a Service (SaaS) cloud model and it can be accessible by any available browser. The developed cloud takes advantage of the Infrastructure as a Service (IaaS) in order to easily scale its capabilities. Virtual Machines are used on demand of the users’ requested pipeline. The implemented techniques were incorporated on a cloud application framework consisting of a web interface where the user can upload data (NIfTI format) and initiate the processing algorithms. The proposed application is providing the following functionalities: a) a user-based authentication and authorization mechanism, b) an application data manager which is a tool enabling the user to manage his/her uploaded image data, c) a “Data Processing Task” where the user is able to select the appropriate processing algorithm to define his/her processing pipeline on demand. Results: The proposed implementation has already been tested in several end-users’ devices executing a pipeline for brain tumor detection in followup scans. A pipeline processing has been implemented, where the user uploads two scans of a patient (before and after treatment), they are registered, normalized and a difference map is calculated for quantitative evaluation of tumor growth or reduction. Results are stored in the cloud storage space and are available to the user anytime, at request. Conclusion: Cloud computing is one of the current biggest trends in Information Technology providing and accessing computing resources via Internet adhoc. Our goal is to offer a cloud implementation providing a way to deploy medical image processing as an application, enabling algorithm sharing without the hustle of maintaining complex infrastructures. This research has been co-financed by the European Union (European Social Fund ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF) Research Funding Program: Thales. Investing in knowledge society through the European Social Fund.

PANDA: a pipeline toolbox for analyzing brain diffusion images

Diffusion magnetic resonance imaging (dMRI) is widely used in both scientific research and clinical practice in in-vivo studies of the human brain. While a number of post-processing packages have been developed, fully automated processing of dMRI datasets remains challenging. Here, we developed a MATLAB toolbox named “Pipeline for Analyzing braiN Diffusion imAges” (PANDA) for fully automated processing of brain diffusion images. The processing modules of a few established packages, including FMRIB Software Library (FSL), Pipeline System for Octave and Matlab (PSOM), Diffusion Toolkit and MRIcron, were employed in PANDA. Using any number of raw dMRI datasets from different subjects, in either DICOM or NIfTI format, PANDA can automatically perform a series of steps to process DICOM/NIfTI to diffusion metrics [e.g., fractional anisotropy (FA) and mean diffusivity (MD)] that are ready for statistical analysis at the voxel-level, the atlas-level and the Tract-Based Spatial Statistics (TBSS)-level and can finish the construction of anatomical brain networks for all subjects. In particular, PANDA can process different subjects in parallel, using multiple cores either in a single computer or in a distributed computing environment, thus greatly reducing the time cost when dealing with a large number of datasets. In addition, PANDA has a friendly graphical user interface (GUI), allowing the user to be interactive and to adjust the input/output settings, as well as the processing parameters. As an open-source package, PANDA is freely available at http://www.nitrc.org/projects/panda/. This novel toolbox is expected to substantially simplify the image processing of dMRI datasets and facilitate human structural connectome studies.

TissueStack: an Open Source HTML5 web based imaging viewer

Event Abstract Back to Event TissueStack: an Open Source HTML5 web based imaging viewer Andrew Janke1*, Meng Kuan Lin1, Oliver Nicolini1, Harald Waxenegger1, Jeremy Ullmann1 and Graham J. Galloway1 1 University of Queensland, Australia Web based viewing of large 3D datasets is now a critical part of imaging research. This has been driven by multiple efforts to make available large collections of histological, optical and electron Microscopy data. It is critical that the web applications to view and serve this data are robust, available on multiple platforms, have a scalable view of data and are intuitive to use for novel users. Existing tools that are capable of presenting tri-planar views of large datasets rely upon custom web extensions such as Adobe Flash, have restrictive dataset size limitations and as such are not tractable on mobile devices. The TissueStack project was born from these goals and is now a mature product. The original use case was a large multi-site study with geographically distant generators of imaging data. This leads to the problem in which no one is sure where or how to access the current version of all the data without having to download TB's of data and trawl through it. This problem is not unique to the neuroimaging field and as such in TissueStack we made extensice use of techniques from the very closely related field of GIS (Graphical Information Systems – ie: online mapping). The server and viewing application have a number of features: Image tiles can be either generated on the fly or Pre-tiling can be initiated via the interface. The tool is HTML5 based and thus runs on mobile devices (iOS, Android, etc). We have tested the TissueStack server on 3D datasets up to 12GB in size over a mobile data link with no significant increase in rendering time (See Figure). It supports embedding, direct links to dataset features, dataset colour mapping, intensity range adjustments, multiple views of datasets, overlays and load balancing across multiple servers. The current server has a plug in architecture and can load files in either MINC or NifTI format. An example server running on a virtual appliance in the Australian Research Cloud can be viewed at http://caivm1.qern.qcif.edu.au. Tissuestack has so far been successfully used for neuroimaging, chemical engineering, electron microscopy, digital curation by museums and animal atlasing data. The main website and more information is available at www.tissuestack.org TissueStack is an Open Source project and is available on GitHub (github.com/NIF-au/TissueStack). All code is licensed via the GPL v3. Figure 1 Figure 2 Acknowledgements This project is supported by the Australian National Data Service (ANDS). ANDS is supported by the Australian Government through the National Collaborative Research Infrastructure Strategy Program and the Education Investment Fund (EIF) Super Science Initiative. Keywords: digital atlasing, HTML5, Web service, Neuroimaging, image view Conference: Neuroinformatics 2013, Stockholm, Sweden, 27 Aug - 29 Aug, 2013. Presentation Type: Demo Topic: Digital atlasing Citation: Janke A, Lin M, Nicolini O, Waxenegger H, Ullmann J and Galloway GJ (2013). TissueStack: an Open Source HTML5 web based imaging viewer. Front. Neuroinform. Conference Abstract: Neuroinformatics 2013. doi: 10.3389/conf.fninf.2013.09.00120 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 29 Apr 2013; Published Online: 11 Jul 2013. * Correspondence: Dr. Andrew Janke, University of Queensland, Brisbane, Australia, a.janke@gmail.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Andrew Janke Meng Kuan Lin Oliver Nicolini Harald Waxenegger Jeremy Ullmann Graham J Galloway Google Andrew Janke Meng Kuan Lin Oliver Nicolini Harald Waxenegger Jeremy Ullmann Graham J Galloway Google Scholar Andrew Janke Meng Kuan Lin Oliver Nicolini Harald Waxenegger Jeremy Ullmann Graham J Galloway PubMed Andrew Janke Meng Kuan Lin Oliver Nicolini Harald Waxenegger Jeremy Ullmann Graham J Galloway Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Quantification of diffusion tensor imaging in normal white matter maturation of early childhood using an automated processing pipeline

The degree and status of white matter myelination can be sensitively monitored using diffusion tensor imaging (DTI). This study looks at the measurement of fractional anistropy (FA) and mean diffusivity (MD) using an automated ROI with an existing DTI atlas. Anatomical MRI and structural DTI were performed cross-sectionally on 26 normal children (newborn to 48 months old), using 1.5-T MRI. The automated processing pipeline was implemented to convert diffusion-weighted images into the NIfTI format. DTI-TK software was used to register the processed images to the ICBM DTI-81 atlas, while AFNI software was used for automated atlas-based volumes of interest (VOIs) and statistical value extraction. DTI exhibited consistent grey-white matter contrast. Triphasic temporal variation of the FA and MD values was noted, with FA increasing and MD decreasing rapidly early in the first 12 months. The second phase lasted 12-24 months during which the rate of FA and MD changes was reduced. After 24 months, the FA and MD values plateaued. DTI is a superior technique to conventional MR imaging in depicting WM maturation. The use of the automated processing pipeline provides a reliable environment for quantitative analysis of high-throughput DTI data. Diffusion tensor imaging outperforms conventional MRI in depicting white matter maturation. • DTI will become an important clinical tool for diagnosing paediatric neurological diseases. • DTI appears especially helpful for developmental abnormalities, tumours and white matter disease. • An automated processing pipeline assists quantitative analysis of high throughput DTI data.

Quantitative Analysis of Dynamic Contrast-Enhanced and Diffusion-Weighted Magnetic Resonance Imaging for Oncology inR

The package dcemriS4 provides a complete set of data analysis tools for quantitative assessment of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Image processing is provided for the ANALYZE and NIfTI data formats as input with all parameter estimates being output in NIfTI format. Estimation of T1 relaxation from multiple flip-angle acquisitions, using either constant or spatially-varying flip angles, is performed via nonlinear regression. Both literature-based and data-driven arterial input functions are available and may be combined with a variety of compartmental models. Kinetic parameters are obtained from nonlinear regression, Bayesian estimation via Markov chain Monte Carlo or Bayesian maximum a posteriori estimation. A non-parametric model, using penalized splines, is also available to characterize the contrast agent concentration time curves. Estimation of the apparent diffusion coefficient (ADC) is provided for diffusion-weighted imaging. Given the size of multi-dimensional data sets commonly acquired in imaging studies, care has been taken to maximize computational efficiency and minimize memory usage. All methods are illustrated using both simulated and real-world medical imaging data available in the public domain.